As businesses rapidly integrate artificial intelligence, a new imperative is emerging: building trust. Drawing lessons from high-stakes fields like aerospace engineering, industry leaders argue that the long-term success of AI depends on establishing rigorous systems for transparency, continuous monitoring, and security from the ground up.
This shift challenges the tech industry's long-standing "move fast and break things" philosophy, suggesting that for AI to reach its full potential, a more deliberate and safety-conscious approach is necessary. The focus is moving from mere functionality to demonstrable reliability, a change driven by both security concerns and the need for customer confidence.
Key Takeaways
- Business leaders are increasingly viewing AI trust not as a compliance hurdle, but as a critical enabler for growth and innovation.
- Parallels are being drawn with aerospace engineering, where every component is meticulously tested and validated before deployment.
- Forecasts predict that by 2027, about 50% of enterprises will deploy AI agents, intensifying the need for reliable trust frameworks.
- A proposed "trust operating system" for AI includes transparency, continuous monitoring, and autonomous validation to ensure safe and secure operations.
The New Frontier of Business Risk
The race to adopt artificial intelligence is well underway. Projections indicate that by 2030, AI agents could perform as much as 30% of all work. This rapid integration promises unprecedented efficiency and innovation, but it also introduces complex risks that many organizations are only beginning to address.
Unlike traditional software, many AI systems can learn, adapt, and make autonomous decisions. This capability, while powerful, creates a challenge for security and governance teams. Existing compliance frameworks like SOC 2 and GDPR were designed for data privacy and security, not for the unique behaviors of generative and agentic AI.
Adam Markowitz, CEO of trust management platform Drata and a former aerospace engineer on NASA's Space Shuttle Program, emphasizes the disparity. He notes that in aerospace, trust is non-negotiable. "Every bolt, every line of code, every system had to be validated and tested carefully, or the shuttle would never leave the launchpad," he explained.
A Lesson from the Launchpad
The comparison to aerospace engineering offers a compelling model for the technology sector. In high-stakes environments like space travel, failure is not an option, and trust is built into every stage of development. This contrasts sharply with the software development mantra of rapid iteration and fixing problems after they appear.
Deploying an untested AI model into a critical business function is being compared to launching an unverified rocket. The potential for immediate and significant damage—from financial loss to reputational collapse—is substantial. A misstep in AI application can erode consumer trust in an organization, an asset that is difficult to regain once lost.
From Cost Center to Growth Engine
Historically, governance, risk, and compliance (GRC) teams were often seen as a cost center—a necessary but cumbersome part of doing business. However, with the rise of AI and complex data ecosystems, this perception is changing. Proving that a company can be trusted with sensitive data and critical operations is now a significant competitive advantage. Companies that can demonstrate a robust security and trust posture are finding it easier to secure partnerships and accelerate sales cycles.
This proactive approach to building trust is already showing tangible business results. Drata, for instance, reports that its customers have realized $18 billion in security-influenced revenue by using its tools to demonstrate their security posture to potential clients. This figure highlights a clear market demand for verifiable trust.
Building a Trust Operating System for AI
To operationalize trust in the age of AI, experts propose a new framework modeled on the principles of mission-critical engineering. This "trust operating system" is not a single piece of software but a comprehensive program built on three core pillars.
1. Radical Transparency
In aerospace, exhaustive documentation is not bureaucracy; it is a tool for accountability. Every decision, test, and modification is recorded. For AI, this translates to complete traceability. Businesses must be able to track an AI's operations from the initial policy to the specific control, the evidence of its function, and the final attestation of its reliability.
This means maintaining clear records of data sources, model training processes, and decision-making logic. When an AI system makes a recommendation or takes an action, the organization must be able to explain why.
2. Continuous Monitoring
A space mission is monitored 24/7. Similarly, trust in AI cannot be a one-time certification. It requires a continuous, ongoing process. Controls and security measures must be monitored in real-time to ensure they are functioning as intended. This approach shifts an organization from a state of last-minute audit preparation to one of perpetual readiness.
The Rise of Agentic AI
The conversation is quickly moving toward "agentic AI," where multiple AI agents, humans, and automated systems interact continuously. This will create tens of thousands of touchpoints, each requiring a layer of validated trust to function securely. Without it, the entire interconnected system becomes vulnerable.
3. Autonomous Validation
Modern rocket engines use embedded computers and sensors to manage their own operations mid-flight without direct human intervention. As AI becomes more integrated into business, trust programs must also become more autonomous. Different systems, whether human-operated or AI-driven, need to be able to validate each other's trustworthiness automatically and without ambiguity.
"If humans, agents, and automated workflows are going to transact, they have to be able to validate trust on their own, deterministically, and without ambiguity."
The Future is Built on Trust
The interdependence of complex systems was the foundation of the space shuttle program. Thousands of components, built by different teams, had to function together flawlessly. Trust was the invisible layer that held everything together. The same principle now applies to the burgeoning AI ecosystem.
As companies navigate this new technological era, the ability to earn and maintain trust in every interaction will be a key differentiator. The tools are powerful and the opportunities are vast, but they can only be fully realized on a solid foundation of proven reliability.
The question for business leaders is no longer if they will adopt AI, but how. Those who embed a culture of transparent, continuous, and autonomous trust into their operations will be the ones to lead the next wave of innovation, ensuring their technological rockets not only launch but also land safely.